While there has not been much work on applying epistemic uncertainty methods to address noisy labels, an abundant of research has been done in deep learning noisy labels ranging from loss function adjustment, robust architecture design, data processing, data filtering and so on. Authors in [1]}, [2]}, [3]}, [4]} devised robust loss function to achieve a smaller risk for unseen clean data when learning with noisy labels.
Sample selection techniques as to filter the clean labels for training and removing the noisy labels have been proposed in [5]}, [6]}, [7]}, [8]}. Sample selection and label correction for spatial computing is studied in [9]}.
Devising loss to estimate noise transition matrix and correct the labels are studied in [10]}, [11]}, [12]}. Semi-supervised learning is another field of techniques on noisy labels, where the noisy labelled data are treated as unlabeled and clean labelled data are as labeled [13]}, [14]}, [15]}.
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